Overview

Dataset statistics

Number of variables27
Number of observations23224
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.0 MiB
Average record size in memory224.0 B

Variable types

Numeric20
Categorical7

Alerts

hospital_admission_id is highly overall correlated with source_dbHigh correlation
admission_age is highly overall correlated with comorbidity_score_valueHigh correlation
weight_admission is highly overall correlated with BMI_admissionHigh correlation
BMI_admission is highly overall correlated with weight_admissionHigh correlation
los_hospital is highly overall correlated with los_ICUHigh correlation
los_ICU is highly overall correlated with los_hospitalHigh correlation
comorbidity_score_value is highly overall correlated with admission_age and 1 other fieldsHigh correlation
pCO2 is highly overall correlated with bmp_bicarbonateHigh correlation
pO2 is highly overall correlated with SaO2 and 1 other fieldsHigh correlation
SaO2 is highly overall correlated with pO2 and 1 other fieldsHigh correlation
SpO2 is highly overall correlated with pO2 and 1 other fieldsHigh correlation
bmp_bicarbonate is highly overall correlated with pCO2High correlation
source_db is highly overall correlated with hospital_admission_id and 1 other fieldsHigh correlation
sex_female is highly overall correlated with GenderHigh correlation
comorbidity_score_name is highly overall correlated with comorbidity_score_value and 1 other fieldsHigh correlation
Gender is highly overall correlated with sex_femaleHigh correlation
source_db is highly imbalanced (62.4%)Imbalance
comorbidity_score_name is highly imbalanced (87.5%)Imbalance
race_ethnicity is highly imbalanced (61.4%)Imbalance
BMI_admission is highly skewed (γ1 = 55.28962907)Skewed
comorbidity_score_value has 1885 (8.1%) zerosZeros
sofa_past_overall_24hr has 2211 (9.5%) zerosZeros

Reproduction

Analysis started2024-02-11 10:50:04.731532
Analysis finished2024-02-11 10:50:26.356568
Duration21.63 seconds
Software versionydata-profiling vv4.6.0
Download configurationconfig.json

Variables

hospital_admission_id
Real number (ℝ)

HIGH CORRELATION 

Distinct23223
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3769312.6
Minimum100292
Maximum29995505
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size362.9 KiB
2024-02-11T14:50:26.403403image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum100292
5-th percentile245871
Q1720774
median1306857
Q32444642.2
95-th percentile25174554
Maximum29995505
Range29895213
Interquartile range (IQR)1723868.2

Descriptive statistics

Standard deviation7254601.3
Coefficient of variation (CV)1.9246484
Kurtosis5.3919167
Mean3769312.6
Median Absolute Deviation (MAD)726583
Skewness2.6659234
Sum8.7538515 × 1010
Variance5.2629239 × 1013
MonotonicityNot monotonic
2024-02-11T14:50:26.474349image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
144855 2
 
< 0.1%
190805 1
 
< 0.1%
2212147 1
 
< 0.1%
2349294 1
 
< 0.1%
2212519 1
 
< 0.1%
2169573 1
 
< 0.1%
2245297 1
 
< 0.1%
2246397 1
 
< 0.1%
2147152 1
 
< 0.1%
2313711 1
 
< 0.1%
Other values (23213) 23213
> 99.9%
ValueCountFrequency (%)
100292 1
< 0.1%
100606 1
< 0.1%
100756 1
< 0.1%
100989 1
< 0.1%
101117 1
< 0.1%
101317 1
< 0.1%
101528 1
< 0.1%
101662 1
< 0.1%
101776 1
< 0.1%
102141 1
< 0.1%
ValueCountFrequency (%)
29995505 1
< 0.1%
29994129 1
< 0.1%
29981470 1
< 0.1%
29981257 1
< 0.1%
29981080 1
< 0.1%
29979324 1
< 0.1%
29965581 1
< 0.1%
29965349 1
< 0.1%
29964086 1
< 0.1%
29961953 1
< 0.1%

source_db
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size362.9 KiB
eicu
20469 
mimic_iv
2359 
mimic_iii
 
396

Length

Max length9
Median length4
Mean length4.4915605
Min length4

Characters and Unicode

Total characters104312
Distinct characters7
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st roweicu
2nd roweicu
3rd roweicu
4th roweicu
5th roweicu

Common Values

ValueCountFrequency (%)
eicu 20469
88.1%
mimic_iv 2359
 
10.2%
mimic_iii 396
 
1.7%

Length

2024-02-11T14:50:26.524911image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-11T14:50:26.564564image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
eicu 20469
88.1%
mimic_iv 2359
 
10.2%
mimic_iii 396
 
1.7%

Most occurring characters

ValueCountFrequency (%)
i 29526
28.3%
c 23224
22.3%
e 20469
19.6%
u 20469
19.6%
m 5510
 
5.3%
_ 2755
 
2.6%
v 2359
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 101557
97.4%
Connector Punctuation 2755
 
2.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 29526
29.1%
c 23224
22.9%
e 20469
20.2%
u 20469
20.2%
m 5510
 
5.4%
v 2359
 
2.3%
Connector Punctuation
ValueCountFrequency (%)
_ 2755
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 101557
97.4%
Common 2755
 
2.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 29526
29.1%
c 23224
22.9%
e 20469
20.2%
u 20469
20.2%
m 5510
 
5.4%
v 2359
 
2.3%
Common
ValueCountFrequency (%)
_ 2755
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 104312
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 29526
28.3%
c 23224
22.3%
e 20469
19.6%
u 20469
19.6%
m 5510
 
5.3%
_ 2755
 
2.6%
v 2359
 
2.3%

admission_age
Real number (ℝ)

HIGH CORRELATION 

Distinct77
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.978901
Minimum14
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size362.9 KiB
2024-02-11T14:50:26.606321image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile35
Q156
median67
Q376
95-th percentile87
Maximum90
Range76
Interquartile range (IQR)20

Descriptive statistics

Standard deviation15.379131
Coefficient of variation (CV)0.23667884
Kurtosis0.15955558
Mean64.978901
Median Absolute Deviation (MAD)10
Skewness-0.64679437
Sum1509070
Variance236.51766
MonotonicityNot monotonic
2024-02-11T14:50:26.656888image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67 685
 
2.9%
90 659
 
2.8%
68 626
 
2.7%
71 616
 
2.7%
65 612
 
2.6%
72 599
 
2.6%
62 598
 
2.6%
66 593
 
2.6%
73 591
 
2.5%
63 589
 
2.5%
Other values (67) 17056
73.4%
ValueCountFrequency (%)
14 1
 
< 0.1%
15 1
 
< 0.1%
16 7
 
< 0.1%
17 7
 
< 0.1%
18 23
 
0.1%
19 40
0.2%
20 44
0.2%
21 45
0.2%
22 64
0.3%
23 83
0.4%
ValueCountFrequency (%)
90 659
2.8%
89 210
 
0.9%
88 241
 
1.0%
87 283
1.2%
86 308
1.3%
85 352
1.5%
84 398
1.7%
83 426
1.8%
82 421
1.8%
81 395
1.7%

sex_female
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size362.9 KiB
0
12896 
1
10328 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23224
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12896
55.5%
1 10328
44.5%

Length

2024-02-11T14:50:26.703402image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-11T14:50:26.741460image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 12896
55.5%
1 10328
44.5%

Most occurring characters

ValueCountFrequency (%)
0 12896
55.5%
1 10328
44.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23224
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12896
55.5%
1 10328
44.5%

Most occurring scripts

ValueCountFrequency (%)
Common 23224
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12896
55.5%
1 10328
44.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23224
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12896
55.5%
1 10328
44.5%

weight_admission
Real number (ℝ)

HIGH CORRELATION 

Distinct2265
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.577624
Minimum0
Maximum639.6
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size362.9 KiB
2024-02-11T14:50:26.780205image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile50.2
Q168
median82.7
Q3101
95-th percentile139
Maximum639.6
Range639.6
Interquartile range (IQR)33

Descriptive statistics

Standard deviation29.407418
Coefficient of variation (CV)0.33578689
Kurtosis14.594963
Mean87.577624
Median Absolute Deviation (MAD)16.3
Skewness1.9799904
Sum2033902.7
Variance864.79621
MonotonicityNot monotonic
2024-02-11T14:50:26.829612image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68 203
 
0.9%
81.6 182
 
0.8%
90.7 172
 
0.7%
63.5 164
 
0.7%
75 159
 
0.7%
100 151
 
0.7%
80 149
 
0.6%
70 143
 
0.6%
90 139
 
0.6%
77.1 126
 
0.5%
Other values (2255) 21636
93.2%
ValueCountFrequency (%)
0 1
< 0.1%
0.5 2
< 0.1%
2.5 1
< 0.1%
10 1
< 0.1%
19.8 1
< 0.1%
22 1
< 0.1%
22.5 1
< 0.1%
25.2 1
< 0.1%
26.02 1
< 0.1%
27.3 1
< 0.1%
ValueCountFrequency (%)
639.6 1
< 0.1%
630.9 1
< 0.1%
362.9 1
< 0.1%
303 1
< 0.1%
302.8 1
< 0.1%
295.4 1
< 0.1%
295.1 1
< 0.1%
294.8 1
< 0.1%
287 1
< 0.1%
285.6 1
< 0.1%

height_admission
Real number (ℝ)

Distinct383
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean169.23499
Minimum1.54
Maximum504.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size362.9 KiB
2024-02-11T14:50:26.880872image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1.54
5-th percentile152.4
Q1162.5
median170
Q3177.8
95-th percentile187
Maximum504.8
Range503.26
Interquartile range (IQR)15.3

Descriptive statistics

Standard deviation12.85682
Coefficient of variation (CV)0.075970223
Kurtosis64.683114
Mean169.23499
Median Absolute Deviation (MAD)7.8
Skewness-0.82887784
Sum3930313.3
Variance165.29781
MonotonicityNot monotonic
2024-02-11T14:50:26.933832image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
160 1338
 
5.8%
177.8 1212
 
5.2%
167.6 1084
 
4.7%
165.1 1068
 
4.6%
172.7 1060
 
4.6%
162.6 819
 
3.5%
170.2 809
 
3.5%
157.5 760
 
3.3%
175.3 751
 
3.2%
182.9 727
 
3.1%
Other values (373) 13596
58.5%
ValueCountFrequency (%)
1.54 1
 
< 0.1%
1.6 2
< 0.1%
1.65 1
 
< 0.1%
1.67 4
< 0.1%
1.7 2
< 0.1%
1.72 1
 
< 0.1%
1.75 1
 
< 0.1%
1.77 1
 
< 0.1%
1.82 1
 
< 0.1%
15.2 1
 
< 0.1%
ValueCountFrequency (%)
504.8 1
 
< 0.1%
504.2 1
 
< 0.1%
257.5 1
 
< 0.1%
254 1
 
< 0.1%
213.4 1
 
< 0.1%
210.8 1
 
< 0.1%
208.3 2
< 0.1%
205.7 2
< 0.1%
203.2 4
< 0.1%
203 2
< 0.1%

BMI_admission
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct15958
Distinct (%)68.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean239.37269
Minimum0
Maximum725551.02
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size362.9 KiB
2024-02-11T14:50:26.986509image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18.910352
Q124.177819
median28.515625
Q334.523103
95-th percentile48.276876
Maximum725551.02
Range725551.02
Interquartile range (IQR)10.345284

Descriptive statistics

Standard deviation9467.2699
Coefficient of variation (CV)39.550334
Kurtosis3474.1604
Mean239.37269
Median Absolute Deviation (MAD)4.9724374
Skewness55.289629
Sum5559191.4
Variance89629199
MonotonicityNot monotonic
2024-02-11T14:50:27.217046image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.29590458 17
 
0.1%
22.60610272 17
 
0.1%
24.01776785 16
 
0.1%
27.35933163 16
 
0.1%
29.93615455 14
 
0.1%
22.79944302 14
 
0.1%
26.539849 14
 
0.1%
26.5625 14
 
0.1%
24.20811 14
 
0.1%
28.40550436 14
 
0.1%
Other values (15948) 23074
99.4%
ValueCountFrequency (%)
0 1
< 0.1%
0.1581635816 1
< 0.1%
0.1627069755 1
< 0.1%
0.7690393376 1
< 0.1%
2.684215682 1
< 0.1%
3.139042594 1
< 0.1%
3.810394757 1
< 0.1%
7.432483297 1
< 0.1%
7.736827222 1
< 0.1%
9.545817908 1
< 0.1%
ValueCountFrequency (%)
725551.0204 1
< 0.1%
657783.7747 1
< 0.1%
588093.2255 1
< 0.1%
385098.0673 1
< 0.1%
321875 1
< 0.1%
283443.4549 1
< 0.1%
279321.5963 1
< 0.1%
263544.7668 1
< 0.1%
260676.2523 1
< 0.1%
237628.4478 1
< 0.1%

los_hospital
Real number (ℝ)

HIGH CORRELATION 

Distinct14989
Distinct (%)64.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.479877
Minimum-0.26458333
Maximum763.275
Zeros2
Zeros (%)< 0.1%
Negative1
Negative (%)< 0.1%
Memory size362.9 KiB
2024-02-11T14:50:27.271002image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-0.26458333
5-th percentile1.6944444
Q14.9310764
median8.2638889
Q314.329167
95-th percentile30.338889
Maximum763.275
Range763.53958
Interquartile range (IQR)9.3980903

Descriptive statistics

Standard deviation13.406565
Coefficient of variation (CV)1.1678318
Kurtosis720.29224
Mean11.479877
Median Absolute Deviation (MAD)4.2229167
Skewness17.167519
Sum266608.65
Variance179.73599
MonotonicityNot monotonic
2024-02-11T14:50:27.324086image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 229
 
1.0%
4 191
 
0.8%
7 173
 
0.7%
6 172
 
0.7%
9 166
 
0.7%
8 158
 
0.7%
10 148
 
0.6%
11 111
 
0.5%
15 101
 
0.4%
12 99
 
0.4%
Other values (14979) 21676
93.3%
ValueCountFrequency (%)
-0.2645833333 1
< 0.1%
0 2
< 0.1%
0.09375 1
< 0.1%
0.1291666667 1
< 0.1%
0.1458333333 1
< 0.1%
0.1680555556 1
< 0.1%
0.1722222222 1
< 0.1%
0.1756944444 1
< 0.1%
0.1854166667 1
< 0.1%
0.1881944444 1
< 0.1%
ValueCountFrequency (%)
763.275 1
< 0.1%
600.7680556 1
< 0.1%
506.5201389 1
< 0.1%
353.8201389 1
< 0.1%
323.9458333 1
< 0.1%
281.0541667 1
< 0.1%
251.9930556 1
< 0.1%
246.9111111 1
< 0.1%
167.5923611 1
< 0.1%
162.5583333 1
< 0.1%

los_ICU
Real number (ℝ)

HIGH CORRELATION 

Distinct1346
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6292447
Minimum0
Maximum506.375
Zeros7
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size362.9 KiB
2024-02-11T14:50:27.382363image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.75
Q11.7916667
median3.4166667
Q36.875
95-th percentile18.083333
Maximum506.375
Range506.375
Interquartile range (IQR)5.0833333

Descriptive statistics

Standard deviation7.5240618
Coefficient of variation (CV)1.3366024
Kurtosis901.46871
Mean5.6292447
Median Absolute Deviation (MAD)2.0416667
Skewness16.459413
Sum130733.58
Variance56.611506
MonotonicityNot monotonic
2024-02-11T14:50:27.433768image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 266
 
1.1%
1 264
 
1.1%
1.833333333 221
 
1.0%
1.916666667 218
 
0.9%
1.75 210
 
0.9%
0.875 208
 
0.9%
1.25 205
 
0.9%
1.958333333 202
 
0.9%
1.875 201
 
0.9%
1.041666667 200
 
0.9%
Other values (1336) 21029
90.5%
ValueCountFrequency (%)
0 7
 
< 0.1%
0.04166666667 4
 
< 0.1%
0.08333333333 11
 
< 0.1%
0.125 25
0.1%
0.1666666667 29
0.1%
0.2083333333 38
0.2%
0.25 51
0.2%
0.2916666667 56
0.2%
0.3333333333 59
0.3%
0.375 56
0.2%
ValueCountFrequency (%)
506.375 1
< 0.1%
246.9166667 1
< 0.1%
101.75 1
< 0.1%
98.66666667 1
< 0.1%
87.58333333 1
< 0.1%
82.83333333 1
< 0.1%
81.25 1
< 0.1%
81.125 1
< 0.1%
78.20833333 1
< 0.1%
76.54166667 1
< 0.1%

comorbidity_score_name
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size362.9 KiB
Charlson
22828 
Elixhauser
 
396

Length

Max length10
Median length8
Mean length8.0341027
Min length8

Characters and Unicode

Total characters186584
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCharlson
2nd rowCharlson
3rd rowCharlson
4th rowCharlson
5th rowCharlson

Common Values

ValueCountFrequency (%)
Charlson 22828
98.3%
Elixhauser 396
 
1.7%

Length

2024-02-11T14:50:27.483144image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-11T14:50:27.525742image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
charlson 22828
98.3%
elixhauser 396
 
1.7%

Most occurring characters

ValueCountFrequency (%)
h 23224
12.4%
a 23224
12.4%
r 23224
12.4%
l 23224
12.4%
s 23224
12.4%
C 22828
12.2%
o 22828
12.2%
n 22828
12.2%
E 396
 
0.2%
i 396
 
0.2%
Other values (3) 1188
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 163360
87.6%
Uppercase Letter 23224
 
12.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
h 23224
14.2%
a 23224
14.2%
r 23224
14.2%
l 23224
14.2%
s 23224
14.2%
o 22828
14.0%
n 22828
14.0%
i 396
 
0.2%
x 396
 
0.2%
u 396
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
C 22828
98.3%
E 396
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 186584
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
h 23224
12.4%
a 23224
12.4%
r 23224
12.4%
l 23224
12.4%
s 23224
12.4%
C 22828
12.2%
o 22828
12.2%
n 22828
12.2%
E 396
 
0.2%
i 396
 
0.2%
Other values (3) 1188
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 186584
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
h 23224
12.4%
a 23224
12.4%
r 23224
12.4%
l 23224
12.4%
s 23224
12.4%
C 22828
12.2%
o 22828
12.2%
n 22828
12.2%
E 396
 
0.2%
i 396
 
0.2%
Other values (3) 1188
 
0.6%

comorbidity_score_value
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct45
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3306493
Minimum-8
Maximum40
Zeros1885
Zeros (%)8.1%
Negative19
Negative (%)0.1%
Memory size362.9 KiB
2024-02-11T14:50:27.567475image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-8
5-th percentile0
Q12
median4
Q36
95-th percentile9
Maximum40
Range48
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.0728425
Coefficient of variation (CV)0.70955698
Kurtosis9.03646
Mean4.3306493
Median Absolute Deviation (MAD)2
Skewness1.6866185
Sum100575
Variance9.4423608
MonotonicityNot monotonic
2024-02-11T14:50:27.618929image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
4 3384
14.6%
3 3378
14.5%
5 3051
13.1%
2 2634
11.3%
6 2369
10.2%
1 1967
8.5%
0 1885
8.1%
7 1673
7.2%
8 1091
 
4.7%
9 642
 
2.8%
Other values (35) 1150
 
5.0%
ValueCountFrequency (%)
-8 2
 
< 0.1%
-5 2
 
< 0.1%
-4 4
 
< 0.1%
-3 3
 
< 0.1%
-2 2
 
< 0.1%
-1 6
 
< 0.1%
0 1885
8.1%
1 1967
8.5%
2 2634
11.3%
3 3378
14.5%
ValueCountFrequency (%)
40 1
 
< 0.1%
38 1
 
< 0.1%
36 1
 
< 0.1%
35 2
< 0.1%
34 1
 
< 0.1%
33 2
< 0.1%
32 1
 
< 0.1%
31 3
< 0.1%
30 2
< 0.1%
29 4
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size362.9 KiB
0.0
18920 
1.0
4304 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters69672
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18920
81.5%
1.0 4304
 
18.5%

Length

2024-02-11T14:50:27.661989image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-11T14:50:27.698711image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18920
81.5%
1.0 4304
 
18.5%

Most occurring characters

ValueCountFrequency (%)
0 42144
60.5%
. 23224
33.3%
1 4304
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46448
66.7%
Other Punctuation 23224
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 42144
90.7%
1 4304
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 23224
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69672
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 42144
60.5%
. 23224
33.3%
1 4304
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69672
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 42144
60.5%
. 23224
33.3%
1 4304
 
6.2%

race_ethnicity
Categorical

IMBALANCE 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size362.9 KiB
White
18288 
Black
1907 
Unknown
 
1666
Hispanic OR Latino
 
881
Asian
 
317
Other values (3)
 
165

Length

Max length34
Median length5
Mean length5.8210041
Min length5

Characters and Unicode

Total characters135187
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWhite
2nd rowWhite
3rd rowWhite
4th rowWhite
5th rowWhite

Common Values

ValueCountFrequency (%)
White 18288
78.7%
Black 1907
 
8.2%
Unknown 1666
 
7.2%
Hispanic OR Latino 881
 
3.8%
Asian 317
 
1.4%
American Indian / Alaska Native 157
 
0.7%
Native Hawaiian / Pacific Islander 6
 
< 0.1%
More Than One Race 2
 
< 0.1%

Length

2024-02-11T14:50:27.733713image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-11T14:50:27.780893image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
white 18288
71.3%
black 1907
 
7.4%
unknown 1666
 
6.5%
hispanic 881
 
3.4%
or 881
 
3.4%
latino 881
 
3.4%
asian 317
 
1.2%
native 163
 
0.6%
163
 
0.6%
indian 157
 
0.6%
Other values (9) 340
 
1.3%

Most occurring characters

ValueCountFrequency (%)
i 21749
16.1%
t 19332
14.3%
e 18620
13.8%
h 18290
13.5%
W 18288
13.5%
n 7564
 
5.6%
a 4811
 
3.6%
k 3730
 
2.8%
c 2959
 
2.2%
o 2549
 
1.9%
Other values (23) 17295
12.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 106242
78.6%
Uppercase Letter 26362
 
19.5%
Space Separator 2420
 
1.8%
Other Punctuation 163
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 21749
20.5%
t 19332
18.2%
e 18620
17.5%
h 18290
17.2%
n 7564
 
7.1%
a 4811
 
4.5%
k 3730
 
3.5%
c 2959
 
2.8%
o 2549
 
2.4%
l 2070
 
1.9%
Other values (8) 4568
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
W 18288
69.4%
B 1907
 
7.2%
U 1666
 
6.3%
H 887
 
3.4%
O 883
 
3.3%
R 883
 
3.3%
L 881
 
3.3%
A 631
 
2.4%
I 163
 
0.6%
N 163
 
0.6%
Other values (3) 10
 
< 0.1%
Space Separator
ValueCountFrequency (%)
2420
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 163
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 132604
98.1%
Common 2583
 
1.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 21749
16.4%
t 19332
14.6%
e 18620
14.0%
h 18290
13.8%
W 18288
13.8%
n 7564
 
5.7%
a 4811
 
3.6%
k 3730
 
2.8%
c 2959
 
2.2%
o 2549
 
1.9%
Other values (21) 14712
11.1%
Common
ValueCountFrequency (%)
2420
93.7%
/ 163
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 135187
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 21749
16.1%
t 19332
14.3%
e 18620
13.8%
h 18290
13.5%
W 18288
13.5%
n 7564
 
5.6%
a 4811
 
3.6%
k 3730
 
2.8%
c 2959
 
2.2%
o 2549
 
1.9%
Other values (23) 17295
12.8%

pH
Real number (ℝ)

Distinct625
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.3596932
Minimum6.683
Maximum7.778
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size362.9 KiB
2024-02-11T14:50:27.833072image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum6.683
5-th percentile7.18
Q17.31
median7.37
Q37.427
95-th percentile7.5
Maximum7.778
Range1.095
Interquartile range (IQR)0.117

Descriptive statistics

Standard deviation0.0990264
Coefficient of variation (CV)0.013455235
Kurtosis2.1235454
Mean7.3596932
Median Absolute Deviation (MAD)0.06
Skewness-0.88055884
Sum170921.52
Variance0.0098062278
MonotonicityNot monotonic
2024-02-11T14:50:27.884487image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.39 816
 
3.5%
7.37 796
 
3.4%
7.4 790
 
3.4%
7.41 782
 
3.4%
7.36 782
 
3.4%
7.38 771
 
3.3%
7.42 727
 
3.1%
7.43 696
 
3.0%
7.34 696
 
3.0%
7.35 680
 
2.9%
Other values (615) 15688
67.6%
ValueCountFrequency (%)
6.683 1
 
< 0.1%
6.742 1
 
< 0.1%
6.8 2
< 0.1%
6.81 1
 
< 0.1%
6.814 1
 
< 0.1%
6.82 2
< 0.1%
6.83 3
< 0.1%
6.84 1
 
< 0.1%
6.848 2
< 0.1%
6.85 1
 
< 0.1%
ValueCountFrequency (%)
7.778 1
< 0.1%
7.75 1
< 0.1%
7.73 1
< 0.1%
7.71 1
< 0.1%
7.705 1
< 0.1%
7.7 1
< 0.1%
7.694 1
< 0.1%
7.69 2
< 0.1%
7.688 1
< 0.1%
7.684 1
< 0.1%

pCO2
Real number (ℝ)

HIGH CORRELATION 

Distinct864
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.640251
Minimum9
Maximum181.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size362.9 KiB
2024-02-11T14:50:27.936365image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile26
Q134.4
median40.7
Q349
95-th percentile73
Maximum181.7
Range172.7
Interquartile range (IQR)14.6

Descriptive statistics

Standard deviation14.754807
Coefficient of variation (CV)0.33810087
Kurtosis4.8317308
Mean43.640251
Median Absolute Deviation (MAD)6.7
Skewness1.7162158
Sum1013501.2
Variance217.70432
MonotonicityNot monotonic
2024-02-11T14:50:27.987785image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38 662
 
2.9%
39 646
 
2.8%
40 593
 
2.6%
36 590
 
2.5%
42 582
 
2.5%
37 570
 
2.5%
41 567
 
2.4%
35 550
 
2.4%
43 546
 
2.4%
34 535
 
2.3%
Other values (854) 17383
74.8%
ValueCountFrequency (%)
9 1
 
< 0.1%
9.3 1
 
< 0.1%
10 1
 
< 0.1%
11.1 1
 
< 0.1%
11.4 1
 
< 0.1%
11.6 1
 
< 0.1%
11.8 1
 
< 0.1%
12 3
< 0.1%
12.2 1
 
< 0.1%
12.8 2
< 0.1%
ValueCountFrequency (%)
181.7 1
< 0.1%
160.8 1
< 0.1%
147.1 1
< 0.1%
147 2
< 0.1%
145.8 1
< 0.1%
145.6 1
< 0.1%
143.9 1
< 0.1%
141 1
< 0.1%
140 1
< 0.1%
139.8 1
< 0.1%

pO2
Real number (ℝ)

HIGH CORRELATION 

Distinct1201
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.593171
Minimum33
Maximum542
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size362.9 KiB
2024-02-11T14:50:28.040399image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile55
Q169
median83
Q3104
95-th percentile166
Maximum542
Range509
Interquartile range (IQR)35

Descriptive statistics

Standard deviation42.674138
Coefficient of variation (CV)0.45595354
Kurtosis18.222585
Mean93.593171
Median Absolute Deviation (MAD)16
Skewness3.3537015
Sum2173607.8
Variance1821.0821
MonotonicityNot monotonic
2024-02-11T14:50:28.090546image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74 375
 
1.6%
75 365
 
1.6%
77 364
 
1.6%
69 357
 
1.5%
81 356
 
1.5%
70 354
 
1.5%
71 352
 
1.5%
79 342
 
1.5%
72 342
 
1.5%
82 340
 
1.5%
Other values (1191) 19677
84.7%
ValueCountFrequency (%)
33 3
 
< 0.1%
33.5 1
 
< 0.1%
34 2
 
< 0.1%
35 5
< 0.1%
36 7
< 0.1%
37 8
< 0.1%
37.3 1
 
< 0.1%
37.4 1
 
< 0.1%
37.7 1
 
< 0.1%
38 6
< 0.1%
ValueCountFrequency (%)
542 1
< 0.1%
539 1
< 0.1%
529 1
< 0.1%
515 1
< 0.1%
510 2
< 0.1%
509 1
< 0.1%
492 1
< 0.1%
491 1
< 0.1%
486 2
< 0.1%
484.3 1
< 0.1%

SaO2
Real number (ℝ)

HIGH CORRELATION 

Distinct268
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.682279
Minimum70
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size362.9 KiB
2024-02-11T14:50:28.142014image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile86.7
Q193
median96
Q397.6
95-th percentile99.1
Maximum100
Range30
Interquartile range (IQR)4.6

Descriptive statistics

Standard deviation4.2425701
Coefficient of variation (CV)0.044808492
Kurtosis5.2782389
Mean94.682279
Median Absolute Deviation (MAD)2
Skewness-1.8884643
Sum2198901.3
Variance17.999401
MonotonicityNot monotonic
2024-02-11T14:50:28.192819image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
97 2215
 
9.5%
96 2024
 
8.7%
98 1906
 
8.2%
95 1747
 
7.5%
94 1421
 
6.1%
99 1340
 
5.8%
93 1046
 
4.5%
92 831
 
3.6%
91 688
 
3.0%
100 643
 
2.8%
Other values (258) 9363
40.3%
ValueCountFrequency (%)
70 6
< 0.1%
70.1 2
 
< 0.1%
70.2 2
 
< 0.1%
70.3 1
 
< 0.1%
70.4 1
 
< 0.1%
70.5 1
 
< 0.1%
70.6 1
 
< 0.1%
70.7 1
 
< 0.1%
71 9
< 0.1%
71.1 2
 
< 0.1%
ValueCountFrequency (%)
100 643
2.8%
99.9 27
 
0.1%
99.8 36
 
0.2%
99.7 60
 
0.3%
99.6 61
 
0.3%
99.5 43
 
0.2%
99.4 78
 
0.3%
99.3 87
 
0.4%
99.2 75
 
0.3%
99.1 84
 
0.4%

SpO2
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.575698
Minimum70
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size362.9 KiB
2024-02-11T14:50:28.245666image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile89
Q194
median97
Q398
95-th percentile99
Maximum99
Range29
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.677201
Coefficient of variation (CV)0.038474226
Kurtosis8.1194167
Mean95.575698
Median Absolute Deviation (MAD)2
Skewness-2.2711975
Sum2219650
Variance13.521807
MonotonicityNot monotonic
2024-02-11T14:50:28.290224image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
99 4468
19.2%
98 3827
16.5%
97 3381
14.6%
96 2783
12.0%
95 2280
9.8%
94 1776
 
7.6%
93 1300
 
5.6%
92 968
 
4.2%
91 683
 
2.9%
90 450
 
1.9%
Other values (20) 1308
 
5.6%
ValueCountFrequency (%)
70 5
 
< 0.1%
71 16
0.1%
72 18
0.1%
73 15
0.1%
74 9
 
< 0.1%
75 19
0.1%
76 14
0.1%
77 29
0.1%
78 28
0.1%
79 17
0.1%
ValueCountFrequency (%)
99 4468
19.2%
98 3827
16.5%
97 3381
14.6%
96 2783
12.0%
95 2280
9.8%
94 1776
 
7.6%
93 1300
 
5.6%
92 968
 
4.2%
91 683
 
2.9%
90 450
 
1.9%

vitals_tempc
Real number (ℝ)

Distinct1677
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.855738
Minimum23.7
Maximum44.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size362.9 KiB
2024-02-11T14:50:28.341397image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum23.7
5-th percentile35.72
Q136.5
median36.8
Q337.2
95-th percentile38.2
Maximum44.3
Range20.6
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.8427793
Coefficient of variation (CV)0.022866976
Kurtosis10.163144
Mean36.855738
Median Absolute Deviation (MAD)0.4
Skewness-1.0408051
Sum855937.67
Variance0.71027696
MonotonicityNot monotonic
2024-02-11T14:50:28.391059image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.8 1360
 
5.9%
36.7 1317
 
5.7%
36.6 1317
 
5.7%
36.9 1184
 
5.1%
36.4 1072
 
4.6%
37 1026
 
4.4%
36.5 924
 
4.0%
37.1 886
 
3.8%
37.2 875
 
3.8%
36.3 703
 
3.0%
Other values (1667) 12560
54.1%
ValueCountFrequency (%)
23.7 1
< 0.1%
29.1 1
< 0.1%
29.27777778 1
< 0.1%
29.3 1
< 0.1%
29.7 1
< 0.1%
30.1 1
< 0.1%
30.2 1
< 0.1%
30.4 1
< 0.1%
30.5 2
< 0.1%
30.6 1
< 0.1%
ValueCountFrequency (%)
44.3 1
 
< 0.1%
41.1 1
 
< 0.1%
40.9 2
 
< 0.1%
40.5 3
< 0.1%
40.4 1
 
< 0.1%
40.3 5
< 0.1%
40.281 1
 
< 0.1%
40.28 1
 
< 0.1%
40.27 1
 
< 0.1%
40.2 3
< 0.1%

cbc_hemoglobin
Real number (ℝ)

Distinct662
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.286364
Minimum3
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size362.9 KiB
2024-02-11T14:50:28.441917image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile7.8
Q19.7
median11.2
Q312.7
95-th percentile15.3
Maximum23
Range20
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2798516
Coefficient of variation (CV)0.20200053
Kurtosis0.21051878
Mean11.286364
Median Absolute Deviation (MAD)1.5
Skewness0.37957961
Sum262114.52
Variance5.1977232
MonotonicityNot monotonic
2024-02-11T14:50:28.492059image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.2 386
 
1.7%
10.9 384
 
1.7%
10.2 361
 
1.6%
10.4 355
 
1.5%
10.5 354
 
1.5%
10.3 352
 
1.5%
11.5 349
 
1.5%
11.1 348
 
1.5%
10 343
 
1.5%
9.6 339
 
1.5%
Other values (652) 19653
84.6%
ValueCountFrequency (%)
3 1
 
< 0.1%
3.6 1
 
< 0.1%
4.1 2
< 0.1%
4.4 2
< 0.1%
4.5 3
< 0.1%
4.6 1
 
< 0.1%
4.7 1
 
< 0.1%
4.8 2
< 0.1%
4.9 3
< 0.1%
5 3
< 0.1%
ValueCountFrequency (%)
23 1
< 0.1%
22.8 1
< 0.1%
22.4 1
< 0.1%
21.6 1
< 0.1%
21.4 1
< 0.1%
21.1 1
< 0.1%
21 1
< 0.1%
20.9 1
< 0.1%
20.6 1
< 0.1%
20.4 2
< 0.1%

bmp_sodium
Real number (ℝ)

Distinct221
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean138.4241
Minimum100
Maximum183
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size362.9 KiB
2024-02-11T14:50:28.549648image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile130
Q1136
median138.65
Q3141
95-th percentile146
Maximum183
Range83
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.2913293
Coefficient of variation (CV)0.038225493
Kurtosis3.6137219
Mean138.4241
Median Absolute Deviation (MAD)2.65
Skewness-0.08105714
Sum3214761.3
Variance27.998166
MonotonicityNot monotonic
2024-02-11T14:50:28.601270image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
138 1956
 
8.4%
139 1904
 
8.2%
140 1862
 
8.0%
137 1704
 
7.3%
141 1626
 
7.0%
136 1508
 
6.5%
142 1342
 
5.8%
135 1120
 
4.8%
143 1031
 
4.4%
134 983
 
4.2%
Other values (211) 8188
35.3%
ValueCountFrequency (%)
100 1
 
< 0.1%
103 1
 
< 0.1%
106 1
 
< 0.1%
108 2
 
< 0.1%
109 5
< 0.1%
110 3
< 0.1%
111 3
< 0.1%
112 5
< 0.1%
113 3
< 0.1%
114 4
< 0.1%
ValueCountFrequency (%)
183 1
 
< 0.1%
175 1
 
< 0.1%
174 1
 
< 0.1%
173 2
< 0.1%
172 1
 
< 0.1%
171 2
< 0.1%
170 2
< 0.1%
169 1
 
< 0.1%
168 4
< 0.1%
166 1
 
< 0.1%

bmp_bicarbonate
Real number (ℝ)

HIGH CORRELATION 

Distinct524
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.476945
Minimum2
Maximum63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size362.9 KiB
2024-02-11T14:50:28.651258image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile15
Q121
median24
Q327.2
95-th percentile35
Maximum63
Range61
Interquartile range (IQR)6.2

Descriptive statistics

Standard deviation5.9028848
Coefficient of variation (CV)0.24116101
Kurtosis1.5982854
Mean24.476945
Median Absolute Deviation (MAD)3
Skewness0.4952669
Sum568452.56
Variance34.844048
MonotonicityNot monotonic
2024-02-11T14:50:28.701158image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 1730
 
7.4%
23 1655
 
7.1%
25 1551
 
6.7%
22 1482
 
6.4%
26 1300
 
5.6%
21 1245
 
5.4%
27 1092
 
4.7%
20 1022
 
4.4%
28 880
 
3.8%
19 871
 
3.8%
Other values (514) 10396
44.8%
ValueCountFrequency (%)
2 1
 
< 0.1%
3 1
 
< 0.1%
4 4
 
< 0.1%
5 9
 
< 0.1%
6 16
 
0.1%
7 21
 
0.1%
8 41
0.2%
8.2 1
 
< 0.1%
8.7 1
 
< 0.1%
9 62
0.3%
ValueCountFrequency (%)
63 1
 
< 0.1%
58 1
 
< 0.1%
57 1
 
< 0.1%
55 1
 
< 0.1%
54.6 1
 
< 0.1%
53 3
 
< 0.1%
52 1
 
< 0.1%
51 2
 
< 0.1%
50 6
< 0.1%
49 8
< 0.1%

bmp_creatinine
Real number (ℝ)

Distinct1898
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.558508
Minimum0.1
Maximum22.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size362.9 KiB
2024-02-11T14:50:28.753177image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.5
Q10.8
median1.09
Q31.7
95-th percentile4.2
Maximum22.99
Range22.89
Interquartile range (IQR)0.9

Descriptive statistics

Standard deviation1.4945189
Coefficient of variation (CV)0.95894209
Kurtosis25.295763
Mean1.558508
Median Absolute Deviation (MAD)0.38
Skewness4.0417656
Sum36194.79
Variance2.2335868
MonotonicityNot monotonic
2024-02-11T14:50:28.802109image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8 745
 
3.2%
0.7 685
 
2.9%
0.9 682
 
2.9%
1 614
 
2.6%
1.1 484
 
2.1%
0.6 451
 
1.9%
1.2 400
 
1.7%
1.3 361
 
1.6%
0.5 329
 
1.4%
1.4 280
 
1.2%
Other values (1888) 18193
78.3%
ValueCountFrequency (%)
0.1 4
 
< 0.1%
0.11 1
 
< 0.1%
0.14 1
 
< 0.1%
0.15 2
 
< 0.1%
0.17 3
 
< 0.1%
0.18 2
 
< 0.1%
0.19 2
 
< 0.1%
0.2 14
0.1%
0.21 3
 
< 0.1%
0.22 3
 
< 0.1%
ValueCountFrequency (%)
22.99 1
< 0.1%
20.22 1
< 0.1%
20.01 1
< 0.1%
19.35 1
< 0.1%
18.8 1
< 0.1%
18.51 1
< 0.1%
18.43 1
< 0.1%
18.2 2
< 0.1%
17.62 1
< 0.1%
17.56 1
< 0.1%

sofa_past_overall_24hr
Real number (ℝ)

ZEROS 

Distinct25
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8344385
Minimum0
Maximum24
Zeros2211
Zeros (%)9.5%
Negative0
Negative (%)0.0%
Memory size362.9 KiB
2024-02-11T14:50:28.844958image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q37
95-th percentile11
Maximum24
Range24
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.4609578
Coefficient of variation (CV)0.71589655
Kurtosis0.62596017
Mean4.8344385
Median Absolute Deviation (MAD)2
Skewness0.77139861
Sum112275
Variance11.978229
MonotonicityNot monotonic
2024-02-11T14:50:28.889801image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
4 2902
12.5%
5 2648
11.4%
3 2432
10.5%
1 2262
9.7%
6 2239
9.6%
0 2211
9.5%
2 1984
8.5%
7 1782
7.7%
8 1465
6.3%
9 1010
 
4.3%
Other values (15) 2289
9.9%
ValueCountFrequency (%)
0 2211
9.5%
1 2262
9.7%
2 1984
8.5%
3 2432
10.5%
4 2902
12.5%
5 2648
11.4%
6 2239
9.6%
7 1782
7.7%
8 1465
6.3%
9 1010
 
4.3%
ValueCountFrequency (%)
24 1
 
< 0.1%
23 2
 
< 0.1%
22 2
 
< 0.1%
21 1
 
< 0.1%
20 9
 
< 0.1%
19 14
 
0.1%
18 26
 
0.1%
17 32
 
0.1%
16 57
0.2%
15 117
0.5%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size362.9 KiB
1.0
12035 
0.0
8084 
4.0
1724 
3.0
 
1059
2.0
 
322

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters69672
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 12035
51.8%
0.0 8084
34.8%
4.0 1724
 
7.4%
3.0 1059
 
4.6%
2.0 322
 
1.4%

Length

2024-02-11T14:50:28.930718image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-11T14:50:28.971876image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 12035
51.8%
0.0 8084
34.8%
4.0 1724
 
7.4%
3.0 1059
 
4.6%
2.0 322
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 31308
44.9%
. 23224
33.3%
1 12035
 
17.3%
4 1724
 
2.5%
3 1059
 
1.5%
2 322
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46448
66.7%
Other Punctuation 23224
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 31308
67.4%
1 12035
 
25.9%
4 1724
 
3.7%
3 1059
 
2.3%
2 322
 
0.7%
Other Punctuation
ValueCountFrequency (%)
. 23224
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69672
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 31308
44.9%
. 23224
33.3%
1 12035
 
17.3%
4 1724
 
2.5%
3 1059
 
1.5%
2 322
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69672
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 31308
44.9%
. 23224
33.3%
1 12035
 
17.3%
4 1724
 
2.5%
3 1059
 
1.5%
2 322
 
0.5%

p50
Real number (ℝ)

Distinct4687
Distinct (%)20.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.734268
Minimum10.024611
Maximum99.871457
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size362.9 KiB
2024-02-11T14:50:29.016474image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum10.024611
5-th percentile15.441453
Q120.810132
median24.649023
Q329.138987
95-th percentile45.783019
Maximum99.871457
Range89.846846
Interquartile range (IQR)8.328855

Descriptive statistics

Standard deviation10.834094
Coefficient of variation (CV)0.40525119
Kurtosis11.617392
Mean26.734268
Median Absolute Deviation (MAD)4.0857183
Skewness2.8274042
Sum620876.63
Variance117.37759
MonotonicityNot monotonic
2024-02-11T14:50:29.066184image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.08244352 79
 
0.3%
21.36139503 74
 
0.3%
24.97704049 74
 
0.3%
25.0069639 73
 
0.3%
18.91089602 73
 
0.3%
21.63881574 71
 
0.3%
23.02591932 70
 
0.3%
20.4663275 69
 
0.3%
20.52913289 68
 
0.3%
22.47107789 68
 
0.3%
Other values (4677) 22505
96.9%
ValueCountFrequency (%)
10.0246109 1
 
< 0.1%
10.09805128 7
< 0.1%
10.18985174 1
 
< 0.1%
10.26456644 2
 
< 0.1%
10.28165221 6
< 0.1%
10.31837239 1
 
< 0.1%
10.37595208 1
 
< 0.1%
10.46525314 4
< 0.1%
10.4711443 1
 
< 0.1%
10.53869351 1
 
< 0.1%
ValueCountFrequency (%)
99.87145728 1
< 0.1%
99.74283199 1
< 0.1%
99.71385141 1
< 0.1%
99.61775392 1
< 0.1%
99.59403657 1
< 0.1%
99.52199008 1
< 0.1%
99.5117053 1
< 0.1%
99.4007947 1
< 0.1%
99.36637287 1
< 0.1%
99.26329914 1
< 0.1%

Gender
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size362.9 KiB
Male
12896 
Female
10328 

Length

Max length6
Median length4
Mean length4.8894247
Min length4

Characters and Unicode

Total characters113552
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Male 12896
55.5%
Female 10328
44.5%

Length

2024-02-11T14:50:29.122103image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-11T14:50:29.170036image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
male 12896
55.5%
female 10328
44.5%

Most occurring characters

ValueCountFrequency (%)
e 33552
29.5%
a 23224
20.5%
l 23224
20.5%
M 12896
 
11.4%
F 10328
 
9.1%
m 10328
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 90328
79.5%
Uppercase Letter 23224
 
20.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 33552
37.1%
a 23224
25.7%
l 23224
25.7%
m 10328
 
11.4%
Uppercase Letter
ValueCountFrequency (%)
M 12896
55.5%
F 10328
44.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 113552
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 33552
29.5%
a 23224
20.5%
l 23224
20.5%
M 12896
 
11.4%
F 10328
 
9.1%
m 10328
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 113552
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 33552
29.5%
a 23224
20.5%
l 23224
20.5%
M 12896
 
11.4%
F 10328
 
9.1%
m 10328
 
9.1%

Interactions

2024-02-11T14:50:25.160298image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:06.402247image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:07.664550image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:08.605382image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:09.521682image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:10.422648image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:11.390328image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:12.869173image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:13.756864image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:14.678311image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:15.620084image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:16.526874image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:17.587319image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:18.487791image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:19.383677image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:20.265341image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:21.231278image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:22.117358image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:23.328935image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:24.233954image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:25.206540image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:06.761566image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:07.712980image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:08.652410image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:09.569779image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:10.471550image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:11.438713image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:12.915298image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:13.805965image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:14.749558image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:15.668741image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:16.573041image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:17.634834image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:18.534061image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:19.428717image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:20.315427image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:21.277649image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:22.165141image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:23.375998image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:24.282219image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:25.251504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:06.819211image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2024-02-11T14:50:09.154389image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:10.065175image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:10.994863image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:12.488581image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:13.400322image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:14.320381image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:15.245903image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:16.170314image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:17.056733image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:18.133684image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:19.027213image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:19.910913image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:20.852674image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:21.770267image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:22.952246image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:23.872017image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:24.800924image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:25.737124image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:07.338991image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:08.282455image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:09.201182image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:10.112551image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:11.046686image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:12.537024image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:13.444895image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:14.367028image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:15.295050image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:16.215347image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:17.102824image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:18.178731image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:19.072027image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:19.953761image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:20.901797image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:21.813936image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:23.002539image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:23.915897image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:24.848020image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:25.779342image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:07.383227image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:08.327253image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:09.243851image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:10.154253image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:11.098135image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:12.589428image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:13.487075image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:14.408290image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:15.337443image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:16.257564image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:17.324116image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:18.220184image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:19.113871image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:20.008752image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:20.947159image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:21.854190image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:23.047417image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:23.957687image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:24.890234image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:25.826978image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:07.439032image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:08.379782image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:09.294681image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:10.203172image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:11.155036image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:12.640386image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:13.537195image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:14.458098image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:15.384555image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:16.305807image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:17.371901image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:18.269314image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:19.164182image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:20.056729image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:20.997698image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:21.902026image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:23.099003image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:24.005419image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:24.940661image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:25.867740image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:07.482735image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:08.424640image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:09.337104image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:10.246474image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:11.202161image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:12.684549image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:13.586857image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:14.500901image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:15.427459image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:16.355751image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:17.412508image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:18.310903image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:19.207661image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:20.098045image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:21.042837image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:21.943515image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:23.142144image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:24.046622image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:24.984045image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:25.910854image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:07.529535image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:08.472577image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:09.385980image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:10.292821image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:11.252246image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:12.731950image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:13.630941image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:14.546031image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:15.472471image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:16.400611image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:17.456490image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:18.357831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:19.254627image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:20.142789image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:21.090757image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:21.988591image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:23.194345image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:24.091270image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:25.029500image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:25.953295image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:07.573602image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:08.516793image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:09.432237image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:10.335191image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:11.299476image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:12.778450image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:13.673113image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:14.587816image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:15.519261image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:16.443452image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:17.496419image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:18.402069image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:19.297447image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:20.182619image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:21.137504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:22.030721image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:23.240268image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:24.140492image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:25.073926image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:25.995128image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:07.621062image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:08.563004image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:09.478661image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:10.378555image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:11.345790image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:12.824774image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:13.715954image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:14.635534image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:15.576956image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:16.486224image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:17.540059image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:18.446971image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:19.341812image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:20.225921image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:21.185863image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:22.073763image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:23.286371image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:24.188971image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-11T14:50:25.118409image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2024-02-11T14:50:29.221165image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
hospital_admission_idadmission_ageweight_admissionheight_admissionBMI_admissionlos_hospitallos_ICUcomorbidity_score_valuepHpCO2pO2SaO2SpO2vitals_tempccbc_hemoglobinbmp_sodiumbmp_bicarbonatebmp_creatininesofa_past_overall_24hrp50source_dbsex_femalecomorbidity_score_namein_hospital_mortalityrace_ethnicitysofa_past_cardiovascular_24hrGender
hospital_admission_id1.000-0.0240.0240.0190.0170.0490.0940.0190.014-0.0020.036-0.0560.0020.039-0.047-0.020-0.020-0.0140.0010.0390.7070.0610.0420.0200.0790.1460.061
admission_age-0.0241.000-0.207-0.144-0.1570.005-0.0190.6540.047-0.033-0.0030.022-0.017-0.114-0.1460.0500.0400.1300.0500.0040.0440.0780.0090.1250.0590.0610.078
weight_admission0.024-0.2071.0000.4050.8880.0280.028-0.099-0.0580.147-0.007-0.026-0.0350.0570.100-0.0280.0880.1560.0080.0320.0450.2700.0100.0620.0360.0250.270
height_admission0.019-0.1440.4051.000-0.0120.0070.016-0.0900.018-0.0130.0270.0190.0070.0260.116-0.018-0.0140.1110.0260.0240.0050.2640.0000.0070.0320.0070.264
BMI_admission0.017-0.1570.888-0.0121.0000.0290.023-0.064-0.0680.159-0.018-0.037-0.0400.0530.053-0.0240.0970.121-0.0020.0250.0000.0030.0000.0130.0170.0000.003
los_hospital0.0490.0050.0280.0070.0291.0000.6870.0500.097-0.017-0.008-0.007-0.0150.097-0.191-0.0040.0120.0370.0930.0090.0110.0080.0120.0000.0070.0000.008
los_ICU0.094-0.0190.0280.0160.0230.6871.0000.0310.061-0.012-0.030-0.040-0.0580.099-0.1310.023-0.0080.0450.1350.0380.0300.0050.0150.0160.0330.0100.005
comorbidity_score_value0.0190.654-0.099-0.090-0.0640.0500.0311.0000.0250.003-0.025-0.036-0.035-0.096-0.235-0.0300.0340.2620.1050.0020.3900.0380.5420.1300.0240.0640.038
pH0.0140.047-0.0580.018-0.0680.0970.0610.0251.000-0.419-0.0800.1470.0760.161-0.0540.0170.225-0.204-0.060-0.1750.0830.0500.0000.2170.0220.0640.050
pCO2-0.002-0.0330.147-0.0130.159-0.017-0.0120.003-0.4191.000-0.061-0.160-0.112-0.0710.0970.0520.521-0.106-0.1140.0730.0930.0560.0190.0880.0250.0590.056
pO20.036-0.003-0.0070.027-0.018-0.008-0.030-0.025-0.080-0.0611.0000.8690.5660.0140.0160.014-0.118-0.0020.0450.4420.1040.0270.0350.0420.0100.0340.027
SaO2-0.0560.022-0.0260.019-0.037-0.007-0.040-0.0360.147-0.1600.8691.0000.5710.0400.0170.033-0.057-0.0550.0130.2970.0920.0220.0280.1080.0150.0320.022
SpO20.002-0.017-0.0350.007-0.040-0.015-0.058-0.0350.076-0.1120.5660.5711.0000.006-0.0250.039-0.056-0.0340.017-0.4110.0360.0000.0000.1400.0200.0220.000
vitals_tempc0.039-0.1140.0570.0260.0530.0970.099-0.0960.161-0.0710.0140.0400.0061.0000.0000.0190.042-0.0570.0470.0180.0290.0350.0100.1520.0100.0410.035
cbc_hemoglobin-0.047-0.1460.1000.1160.053-0.191-0.131-0.235-0.0540.0970.0160.017-0.0250.0001.000-0.0240.100-0.156-0.1860.0630.1170.1330.0740.0820.0220.1080.133
bmp_sodium-0.0200.050-0.028-0.018-0.024-0.0040.023-0.0300.0170.0520.0140.0330.0390.019-0.0241.0000.098-0.0660.057-0.0270.0560.0190.0550.0900.0070.0340.019
bmp_bicarbonate-0.0200.0400.088-0.0140.0970.012-0.0080.0340.2250.521-0.118-0.057-0.0560.0420.1000.0981.000-0.287-0.233-0.0570.0980.0750.0580.1790.0280.1140.075
bmp_creatinine-0.0140.1300.1560.1110.1210.0370.0450.262-0.204-0.106-0.002-0.055-0.034-0.057-0.156-0.066-0.2871.0000.3020.0250.0180.0610.0000.1280.0440.0720.061
sofa_past_overall_24hr0.0010.0500.0080.026-0.0020.0930.1350.105-0.060-0.1140.0450.0130.0170.047-0.1860.057-0.2330.3021.0000.0280.1630.0510.1510.2080.0190.3460.051
p500.0390.0040.0320.0240.0250.0090.0380.002-0.1750.0730.4420.297-0.4110.0180.063-0.027-0.0570.0250.0281.0000.0970.0200.0430.1030.0350.0400.020
source_db0.7070.0440.0450.0050.0000.0110.0300.3900.0830.0930.1040.0920.0360.0290.1170.0560.0980.0180.1630.0971.0000.0641.0000.0180.1110.2160.064
sex_female0.0610.0780.2700.2640.0030.0080.0050.0380.0500.0560.0270.0220.0000.0350.1330.0190.0750.0610.0510.0200.0641.0000.0130.0000.0170.0481.000
comorbidity_score_name0.0420.0090.0100.0000.0000.0120.0150.5420.0000.0190.0350.0280.0000.0100.0740.0550.0580.0000.1510.0431.0000.0131.0000.0000.0160.0740.013
in_hospital_mortality0.0200.1250.0620.0070.0130.0000.0160.1300.2170.0880.0420.1080.1400.1520.0820.0900.1790.1280.2080.1030.0180.0000.0001.0000.0000.1850.000
race_ethnicity0.0790.0590.0360.0320.0170.0070.0330.0240.0220.0250.0100.0150.0200.0100.0220.0070.0280.0440.0190.0350.1110.0170.0160.0001.0000.0430.017
sofa_past_cardiovascular_24hr0.1460.0610.0250.0070.0000.0000.0100.0640.0640.0590.0340.0320.0220.0410.1080.0340.1140.0720.3460.0400.2160.0480.0740.1850.0431.0000.048
Gender0.0610.0780.2700.2640.0030.0080.0050.0380.0500.0560.0270.0220.0000.0350.1330.0190.0750.0610.0510.0200.0641.0000.0130.0000.0170.0481.000

Missing values

2024-02-11T14:50:26.076562image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-11T14:50:26.254784image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

hospital_admission_idsource_dbadmission_agesex_femaleweight_admissionheight_admissionBMI_admissionlos_hospitallos_ICUcomorbidity_score_namecomorbidity_score_valuein_hospital_mortalityrace_ethnicitypHpCO2pO2SaO2SpO2vitals_tempccbc_hemoglobinbmp_sodiumbmp_bicarbonatebmp_creatininesofa_past_overall_24hrsofa_past_cardiovascular_24hrp50Gender
9190805eicu60.00125.9172.742.2124986.8840283.291667Charlson3.00.0White7.4139.078.096.096.038.112.8138.021.00.8006.01.024.153464Male
12141654eicu65.0077.1185.422.43029293.04513982.833333Charlson8.00.0White7.2845.0130.095.095.036.69.6132.029.05.1306.01.043.878585Male
13155590eicu44.00262.4190.572.30592230.30347215.458333Charlson0.00.0White7.2942.096.096.098.036.811.4138.023.05.0808.01.022.846133Male
18195394eicu67.00107.1179.133.3886073.9763893.958333Charlson3.00.0White7.3439.069.092.094.036.914.4145.021.01.1307.01.025.006964Male
19138012eicu62.00151.4167.653.89864512.7680564.833333Charlson4.00.0White7.3162.0117.097.097.038.39.7137.824.41.0742.01.032.458224Male
27130585eicu85.0188.0167.031.5536599.9194441.541667Charlson6.00.0Black7.5048.0140.098.099.036.611.7142.040.01.2706.01.025.704131Female
30132653eicu59.0088.7172.729.73986233.21388918.166667Charlson3.00.0White7.2355.096.096.097.036.112.1138.024.01.3301.01.026.632389Male
34163687eicu51.0147.9162.618.11734023.1000004.166667Charlson1.00.0White7.2412.0112.096.098.037.65.3137.06.00.8705.01.026.653822Female
41145590eicu61.0095.1182.928.4284457.9958333.833333Charlson3.00.0White7.3346.0115.097.099.036.79.2139.425.81.3682.00.021.114107Male
46176389eicu51.0053.0175.317.2469391.2763891.291667Charlson1.01.0White7.2548.078.093.078.035.68.8146.014.00.5009.01.048.903332Male
hospital_admission_idsource_dbadmission_agesex_femaleweight_admissionheight_admissionBMI_admissionlos_hospitallos_ICUcomorbidity_score_namecomorbidity_score_valuein_hospital_mortalityrace_ethnicitypHpCO2pO2SaO2SpO2vitals_tempccbc_hemoglobinbmp_sodiumbmp_bicarbonatebmp_creatininesofa_past_overall_24hrsofa_past_cardiovascular_24hrp50Gender
4907528211098mimic_iv74.0160.3160.023.55468814.013.33Charlson11.01.0White7.3450.092.095.097.036.947.8135.025.04.17.01.025.522706Female
4907628592225mimic_iv61.00102.6178.032.3822751.01.38Charlson7.01.0Unknown7.2843.085.094.094.037.909.9135.020.02.311.04.030.805680Male
4908227340968mimic_iv75.0160.8147.028.13642511.01.29Charlson3.00.0Unknown7.3945.0142.099.099.036.679.5132.024.00.64.01.026.071332Female
4908329148528mimic_iv60.0090.0178.028.40550415.06.25Charlson2.00.0White7.5331.070.095.091.036.787.5138.028.00.48.03.029.816750Male
4908426979819mimic_iv84.0086.0178.027.1430376.04.17Charlson5.00.0White7.3735.0137.098.098.037.3210.3140.422.00.98.04.032.603336Male
4908528386154mimic_iv78.0157.0160.022.2656259.09.21Charlson7.01.0Unknown7.3239.083.094.095.036.9011.2138.022.01.211.03.028.014789Female
4908821784060mimic_iv68.0094.7163.035.64304326.018.67Charlson3.00.0Unknown7.3949.087.093.095.037.898.7139.028.01.76.01.029.364899Male
4908921942461mimic_iv84.0081.6168.028.9115657.03.38Charlson5.00.0White7.4633.064.093.096.037.3911.2134.024.00.85.01.019.818227Male
4909028847872mimic_iv76.00107.3183.032.04037112.05.92Charlson10.00.0White7.3851.096.097.098.036.899.6136.030.01.711.03.022.846133Male
4909120617667mimic_iv63.0159.0147.027.30343829.025.33Charlson7.00.0White7.4433.0110.096.099.036.8912.9145.020.01.213.03.020.196103Female